Stock Picking Basics





Kerry Back

Simple Valuation Model

Growing perpetuity

  • Cash flows \(C_1 = c\), \(C_2 = (1+g)c\), \(C_3 = (1+g)^2c\) and so on forever.
  • Discount rate \(r>g\)
  • PV is

\[ c\left[\frac{1}{1+r} + \frac{1+g}{(1+r)^2} + \frac{(1+g)^2}{(1+r)^3} + \cdots\right] = \frac{c}{r-g}\]

Gordon growth model

  • We want to value cash flows to shareholders
  • \(r=\) required return on equity
  • Payouts to shareholders = dividends + repurchases - net issues
  • Assume earnings, payouts, and book equity all grow at rate \(g<r\).
  • Define ROE to be earnings divided by lagged (beginning of year) equity.
  • Set \(k =\) payout ratio \(=\) payouts / earnings.

  • Equity grows by earnings minus payouts = \((1-k) \times\) earnings.
  • Earnings \(=\) ROE \(\times\) lagged equity.
  • \(g=\) % change in equity \(=\) growth in equity / lagged equity

\[=\frac{(1-k) \times \text{ROE} \times \text{lagged equity}}{\text{lagged equity}}\]

\[= (1-k) \times \text{ROE}\]

  • Value of stock is next year’s payout / \((r-g)\).
  • Next year’s payout is \(k\) \(\times\) next year’s earnings.
  • Next year’s earnings \(=\) ROE \(\times\) current book equity.
  • Theoretical price-to-book \(=\) market-to-book

\[=\left.\frac{k \times \text{ROE} \times \text{book equity}}{r-(1-k)\times \text{ROE}}\right/ \text{book equity}\]

\[=\frac{k \times \text{ROE}}{r-(1-k)\times \text{ROE}}\]

Dupont Analysis

\[\text{ROE} = \frac{\text{Net Income}}{\text{Sales}} \times \frac{\text{Sales}}{\text{Lagged Assets}} \] \[\times \frac{\text{Lagged Assets}}{\text{Lagged Equity}}\]


\[= \text{Profit Margin} \times \text{Asset Turnover}\] \[ \times \text{Leverage}\]

Security Analysis

Sell side and buy side

  • Sell-side analysts work for brokerage firms and provide research to brokerage clients.
    • They are a cost center and research is provided free to generate business (= commissions or advising fees).
  • Buy-side analysts work for investment funds who use the research to pick stocks.

Technical versus fundamental

  • Fundamental analysts forecast important ratios and growth rates to produce earnings forecasts and price targets as in Gordon/Dupont.
  • Technical analysts use past prices to generate recommendations.

Technical analysis examples

  • Support and resistance levels.
    • Previous minimum (support) and maximum (resistance) stock prices are regarded as difficult to breach.
    • But if breached, the trend is expected to continue.
  • Moving averages: buy when price rises above moving average and sell when it falls below.
  • Chart patterns (head and shoulders, …)

Quantitative Investing

  • Quantitative investing means using quantifiable signals to pick stocks and/or to time the market.
  • Signals can include ratios and growth rates used by fundamental analysts and price signals used by technical analysts.
  • Signals can also include
    • insider trades, short interest, …
    • sentiment analysis of social media, traditional media, and company announcements
    • satellite and drone image data, and …

Efficient Markets Hypothesis

  • All relevant information is already impounded into prices.
    • Fundamental analysis is futile.
    • Technical analysis is futile.
  • Higher expected returns come only with higher risks: arket risk (beta) and/or other types of risks (oil price, …)

Counter-argument

  • Not all investors are smart
  • Smart investors may not scoop up all opportunities
    • Limited capital
    • Costs of trading
    • For example, an investor who shorts risks running out of capital from margin calls before being eventually right.
  • More likely to be opportunities among smaller stocks, which are difficult for large investors to trade.

Smart beta (factor) investing

  • Groups of stocks with certain characteristics seem to have higher expected returns.
  • These stocks also usually tend to move together.
  • Maybe they are exposed to some risk that some investors regard as undesirable.
  • Maybe you want to take on that risk to get the return.

  • The return of the group of stocks is called a factor.
  • Investing in the factor means you will be correlated with the factor.
    • So, if we regress your return on the factor, you will have a positive slope coefficient (beta).
    • Hence the name “smart beta.”
  • Example: Fama-French factors: Small Minus Big, High book-to-market Minus Low book-to-market, Conservative Minus Agressive, Robust Minus Weak.

Industry examples



Factor investing at BlackRock


Factor investing at AQR

Some data

  • Sort into quintiles each month.
  • Value weighted return of each group
  • Re-sort at the beginning of the next period and continue.

Quantitative investing agenda

  • Find factors worth investing in.
  • Decide how to optimally combine them.
  • Using ML, we can in principle throw in lots of characteristics and let the machine decide which are useful, but preprocessing is usually useful.
  • Need to backtest, which is a variation of the usual ML train-and-test.